System and method for understanding influencer reach within an augmented media intelligence ecosystem

ABSTRACT

Aspects of the present disclosure involve systems, methods, devices, and the like for augmented media intelligence using Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), data analytics and data visualization. In one embodiment, a system is introduced that can retrieve real-time data from social media platforms to perform augmented media intelligence analysis and take real time actions if necessary. In another embodiment, the augmented media intelligence is design to use the machine learning and natural language processing capabilities and social currency means for understanding an influencers reach within the augmented media intelligence system via an influencer score.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is related to and claims benefit of priority to IndianProvisional Application No. 201841022254, filed Jun. 14, 2018, and thisapplication is a continuation in part of U.S. Ser. No. 15/844,257 filedDec. 15, 2017.

TECHNICAL FIELD

The present disclosure generally relates to intelligent informationvisualization for an enterprise system, and more specifically, to dataanalytics and data visualization for understanding influencer reachwithin an augmented media intelligence ecosystem.

BACKGROUND

Today up to one third of the world's population is on a social mediaplatform including social applications, blogs, videos, online news, etc.This data can produce up to 2.5 Exabyte of data per day. Oftentimes,this data is monitored so that if a public relationship crisis or othersignificant event occurs, campaigns and media events can be establishedin response to such crisis. Monitoring the data, however, may be achallenge due to the volume, quality, veracity and speed of datareceived. Further, if a change occurs, the ability to recover from amedia event is essential as the business or its key performanceindicators may be impacted. Thus, it would be beneficial to have thecapability to monitor plan, monitor, and build strategy around thoseindividuals and organizations whose opinions have significant mediareach so that appropriate campaigns and media responses can be created.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 illustrates a flowchart for generating augmented mediaintelligence.

FIG. 2 illustrates a block diagram illustrating a data analytics andvisualization system for augmented media intelligence.

FIG. 3 illustrates monitoring and analysis using augmented mediaintelligence.

FIG. 4 illustrates an exemplary influencer scoring model use tounderstand influencer reach within the augmented media intelligenceecosystem.

FIGS. 5A-5B illustrate exemplary interactive interfaces generated by thedata analytics and visualization system using the classification model.

FIG. 6 illustrates a flow diagram illustrating operations for generatingresilience within the augmented media intelligence ecosystem.

FIG. 7 illustrates an example block diagram of a computer systemsuitable for implementing one or more devices of the communicationsystems of FIGS. 1-6.

Embodiments of the present disclosure and their advantages are bestunderstood by referring to the detailed description that follows. Itshould be appreciated that like reference numerals are used to identifylike elements illustrated in one or more of the figures, whereasshowings therein are for purposes of illustrating embodiments of thepresent disclosure and not for purposes of limiting the same.

DETAILED DESCRIPTION

In the following description, specific details are set forth describingsome embodiments consistent with the present disclosure. It will beapparent, however, to one skilled in the art that some embodiments maybe practiced without some or all of these specific details. The specificembodiments disclosed herein are meant to be illustrative but notlimiting. One skilled in the art may realize other elements that,although not specifically described here, are within the scope and thespirit of this disclosure. In addition, to avoid unnecessary repetition,one or more features shown and described in association with oneembodiment may be incorporated into other embodiments unlessspecifically described otherwise or if the one or more features wouldmake an embodiment non-functional.

Aspects of the present disclosure involve systems, methods, devices, andthe like for augmented media intelligence using Artificial Intelligence(AI), Machine Learning (ML), Natural Language Processing (NLP), dataanalytics and data visualization. In one embodiment, a system isintroduced that can retrieve real-time data from social media platformsto perform augmented media intelligence analysis and take real timeactions if necessary. In another embodiment, the augmented mediaintelligence is design to use the machine learning and natural languageprocessing capabilities and social currency means for understanding aninfluencers reach within the augmented media intelligence system via aninfluencer score. The influencer score along with the real-time data maybe presented as a chart, graph, plot and the like where the augmentedmedia system is designed to generate dashboards, and reports for uservisualization on an interactive user interface, where the reports arebased in part on the influencer data determined, retrieved, measured,and categorized.

Enterprise media generally relates to all forms of digital mediaincluding social media, blogs, videos, online news, etc. In particular,enterprise social media relates to a category of online communicationswhich includes corporate based input, interactions, content-sharing, andcollaboration amongst various venues. The data generated can be veryuseful in understanding responses to product releases, content-sharing,strategy, response to crisis, etc. However, the data is very voluminousand is not always structured. Therefore, a method for ingesting largevolumes of multifaceted data, categorizing and classifying it, andunderstanding its impact is important. Further, understanding how torecover from a media event is essential as it can impact a businessand/or the business key performance indicators. Therefore, it would beimportant to understand how to plan, prepare, and recover after a mediaevent is important. For example, if a negative event occurs,understanding how to recover needs to be understood. Alternatively, if apositive event occurs, understanding how to prolong the event so thatthe engagement may be maximized needs to be understood. This type ofinformation can be captured and appropriate plan can be put in placewith the understanding of an influencer's reach within an augmentedmedia intelligence system.

Conventionally, in social media enterprise, such data can be analyzedusing one or more of five social media available. FIG. 1 presents thefive media analytic methods available. In particular, FIG. 1 illustratesa flowchart 100 for generating augmented media intelligence byintegrating not only the five media analytic methods, but also an addinga fifth Cognitive media analytical method. Further, FIG. 1 presentsflowchart 100 that enables the use of all five media analytic methods toenable augmented media intelligence in a self-sustaining ecosystem.

As illustrated, data analytics can begin with descriptive analytics 102.Descriptive analytics is the analysis of events after they have takenplace. For example, media posts, mentions, views, comments, page views,and the like, can be analyzed to decipher what happened based on thedata retrieved. The data retrieved may derive from one or more serves,devices, systems, clouds, etc., which can include enterprise media.Next, the data retrieved may be analyzed using diagnostic analytics 104.Diagnostic analytics 104 are useful in determining why an event,response, comment, or other occurred. Diagnostic analytics 104 involveslearning based on the monitoring why a result occurred and what did/didnot work. Because the analytics includes learning from the dataretrieved, machine learning algorithms and even statistics indetermining correlations between media sentiments and the businessimpact on key performance indicators (KPIs). Upon retrieving andanalyzing the, what and why of the data, predictive analytics 106 may beperformed to determine the what/why will happen in future. Predictiveanalytics 106 is the analysis of the data retrieved to predict futureevents. For example, predictive analytics 106 may be used to predict themedia impact of a given campaign. That is to say, using historical data,media responses, and large data analysis, predictions can be made as tohow a product release, post, announcement, or campaign will be receivedin media and might translate into a future event. Next, prescriptiveanalytics 108 may be performed on the enterprise data. Prescriptiveanalytics 108 extends the analysis of historical trends from the dataretrieved to discover trends and patterns of behavior in the data. Thepatterns and trends identified can then be used to provide insightand/or prescribe future events, responses, postings, etc. For example,prescriptive analysis 108 may be used to recommend a future campaign forthe business. Finally, the last of the fifth social media analytics,Cognitive Analytics 110 continues the analysis by taking into accountthe reason for a user's behavior and use the analysis to decipher theemotional, psychical, intellectual, and subconscious reasons for thesame. The information gathered from the cognitive analytics 110 can thenbe used for example, to aid marketers in delivering real-timepersonalized experiences to customers.

Note that the descriptive and diagnostic analytics 102,104 can becategorized as reactive analytics as a “look back” at the data retrievedfrom the media sources is analyzed. Alternatively, the predictive,prescriptive analytics, and cognitive analytics 106,108, and 110 can becategorized as proactive analytics as a “look ahead” on how to respondbased on the data retrieved is considered.

FIG. 1 illustrates data analytics that can occur from enterprise data,however, due to the volume, veracity, and speed of data, data ingestionis possible through the creation of a media intelligence platform whichcan deliver this capability in real-time. For example, in descriptiveanalytics, the probability of an event occurring is possible withreal-time listening and monitoring of the enterprise data. As anotherexample, cognitive analytics may be performed using the real-time datato predict and analyze patterns in the data.

FIG. 2 illustrates a system designed to function as a media intelligenceplatform 200 for real-time data analytics. In particular, FIG. 2illustrates a block diagram illustrating a data analytics andvisualization system for augmented media intelligence. The mediaintelligence platform 200 can include at least a database(s) 216, anaugmented media system 202, and/or external peripherals 220-224. Theaugmented media system 202 can be a system design to enable thereal-time presentation, analytics, and visualization of media data. Theaugmented media system can include a social currency module 204,analytics module 206, data tracker 208, Application ProgrammingInterface (API) 210, web server 212, and server 214. The augmented mediasystem 202 can perform the real-time analytics included in FIG. 1 usingat least analytics module 206. In particular, descriptive analytics 102,diagnostics analytics 104, predictive analytics 106 and prescriptiveanalytics can occur on the analytics module 206 for monitoring,responding, predicting and prescribing how to respond to a campaign,event, feedback, etc. etc. To perform such analytics, the analyticsmodule 206 may include an artificial intelligence engine with naturallanguage processing capabilities in order to respond to complex queriesand perform the real-time analytics for the augmented media system 202.

As illustrated, the augmented media system 202 can also include anapplication programming interface (API) module 210. The API module 210can act as an interface with one or more database(s) 216. In addition,API module can enable data tracker module 208 to retrieve data fromdatabase nodes and/or monitor movements of the data across the databasenodes and other media data deriving from the network(s) 218. In someembodiments, the API module 210 may establish a universal protocol forcommunication of data between the API module 210 and each of thedatabase(s) 216 and/or nodes. In other embodiments, the API module 210may generate a data request (e.g., a query) in any one of severalformats corresponding to the database 216. Based on a request for dataintending for a specific database from the data tracker module 208, theAPI module 210 may convert the request to a data query in a format(e.g., an SQL query, a DMX query, a Gremlin query, a LINQ query, and thelike) corresponding to the specific database. Additionally, the server214 may store, and retrieve data previously stored for use with theanalytics module 206.

In some embodiments, the augmented media system 202 can communicate withexternal devices, components, peripherals 220-224 via API module 210.API module 210 can, therefore, act as an interface between one or morenetworks 218 (and systems/peripherals 220-224) and augmented mediasystem 202. Peripherals 220-224 can include networks, servers, systems,computers, devices, clouds, and the like which can be used tocommunicate digital media. For example, peripherals 220-224 can be usedto communicate digital media including but not limited to, social media,blogs, videos, online news, etc. The data communicated (e.g., scraped)from the web over the network 218 can be used for the real-timepresentation, analytics, and visualization of media data.

The augmented media system 202, as indicated, includes a server 214 andnetwork 218 and thus can be a network-based system which can provide thesuitable interfaces that enable the communication using various modes ofcommunication including one or more networks 218. The augmented mediasystem 202 can include the web server 212, and API module 210 tointerface with the at least one server 214. It can be appreciated thatweb server 212 and the API module 210 may be structured, arranged,and/or configured to communicate with various types of devices,third-party devices, third-party applications, client programs, mobiledevices and other peripherals 220-224 and may interoperate with eachother in some implementations.

Web server 212 may be arranged to communicate with other devices andinterface using a web browser, web browser toolbar, desktop widget,mobile widget, web-based application, web-based interpreter, virtualmachine, mobile applications, and so forth. Additionally, API module 210may be arranged to communicate with various client programs and/orapplications comprising an implementation of an API for network-basedsystem and augmented media system 202. For example the augmented mediasystem 202 may be designed to provide an application with an interactiveweb interface, platform, and/or browser by using the web server 212. Theinteractive web interface, may enable a user to view different reportsor performance metrics related to a particular organization group. Forexample, a Marketing or Product Group within a corporation may benefitfrom real-time media data that can be tailored to provide plots,statistics, diagrams, and other information that can be used to market anew campaign or track product performance. In particular, in oneembodiment, a marketing team for example may use the augmented mediasystem to publish and monitor content across social media channelsdriving campaign activation and to provide insights on trends andaudience engagement based on the content published. Therefore, in thisembodiment, the marketing team can use the augmented media system 202 toactively monitor and listen to the social media traffic (internally andexternally) and measure and analyze the performance of a campaign. Asanother example, the interactive web interface may be used by thecustomer service team to service and answer questions from customers andprospective clients. Still in another example, the interactive webinterface may be used to correlate a campaign to the call volume atcustomer service centers. The correlation data can be used to predict,forecast, and prescribe staffing at customer service centers.

In some embodiments, understanding the client and/or customer isimportant for determining how to respond and/or present information.Therefore, in some embodiments, the augmented media system 202 can alsoinclude the social currency module 204. The social currency module 204is a component designed to aid in providing hyper-personalized contentto one or more users in real-time (at the right time) using augmentedmedia system 202. In general, social currency can be described as theresponse and resources that arise from content and information sharedabout a brand or other through social networks, communities, and othersocial media. Therefore, the social currency module 204 is a componentthat evaluates social media users and organizations beneficiating fromsocial media to provide hyper-personalized content in real time in aneffort to deliver content that can help increase a user's propensity toengage in a purchase or respond to a product, campaign, or other. Thesocial currency module 204 can provide the content by evaluating: 1) auser's affiliation to a community, 2) listening to conversations andinteractions among individuals, 3) through group and informationsharing, 4) through monitoring for advocating related to a brand, and 5)detecting knowledge sharing in a given area. Evaluating the user andcontent using the social currency elements mentioned provides theopportunity to identify the user, analyze their social behavior, andengage them, to influence a successful outcome. The social currencymodule 204 can work in conjunction with the analytics module 206 anddata tracker 208 to listen, monitor, analyze, and categorize the mediadata to deliver insights via platforms on a dashboard and/or viareports. In some embodiments, the augmented media system 202 operates inreal-time by scraping social media and analyzing the digital data forthe presentation in an organized report, dashboard, or other platform.

FIG. 3 presents the process for the augmented media system 202 as atechnical solution and media platform designed to provide content in atime sensitive manner. In particular, FIG. 3 illustrates a system 300for the monitoring and analysis performed using augmented mediaintelligence. As previously indicated, the media data 302 may arrivefrom external sources and/or peripherals 220-224 via one or networks 218which scrape and ingest data regarding a particular company, platform,campaign, product, etc., of interest. In some instances, the media data302 obtained is classified and stored in a database 216 for performingthe data analytics, and for building machine learning algorithms fordeeper insights. In some instances, the media data 302 may be stored indatabase 216 and classified into a corresponding library based on thecontent. In other instances, database 216 may also be used to storeother enterprise business data which can be relevant in the dataanalytics resulting from machine learning co-relation and causationdiscovery. For example, key performance indicators (KPIs) may be storedand used during the data analytics in conjunction with artificialintelligence and algorithms to determine the impact by the media.Classification and data analytics may be performed using statisticalmodels, neural networks, and other machine learning algorithms wheretrends, graphs, and correlations can be obtained.

As illustrated in FIG. 3, the media data 302 stored and/or retrieved mayproceed to an application programming interface 210 where the database216 and external devices can interact with the augmented media system202. The API 210 can simultaneously communicate with at least the datatracker 208. Further, the APIs can be used to build a user experienceand solution on the platform. The API 210 also communicates with atleast a data tracker 208. As previously indicated, the API 210 canenable the data tracker module 208 to retrieve data from database nodes,servers, and external devices, and/or monitor movements of the dataacross the database nodes and other media data deriving from thenetwork(s) 218. The data tracker 208 enables the ability to trackinfluencers and others who can impact a company, brand, sentiment, orthe like and allows the opportunity to manage those making an impactpro-actively to deliver value. Monitoring and listening via the datatracker also provides groups within an organization, for example, acommunications team, with insight and analysis of the media data 302 viaa media platform.

Following data tracking, the system 300 may continue to the dataanalysis portion of the process of computing the analytics desired by ateam, organization, group, individual, corporation or the like. Asindicated, data analyzer 206 (e.g., analytics module 206) can bedesigned to perform the real-time analytics desired in a platformdesigned for augmented media intelligence. In particular, descriptiveanalytics 102, diagnostics analytics 104, predictive analytics 106,prescriptive analytics and cognitive analytics 107 can occur on theanalytics module 206 for monitoring, responding, predicting andprescribing how to respond to a campaign, event, feedback, etc. Toperform such analytics, the data analyzer 206 may include an artificialintelligence engine with natural language processing capabilities inorder to respond to complex queries. Additionally, statisticalanalytical models may also be used in such analytics. For example, thestatistical analytical models may be used to identify trends and/orlocate outliers. In addition, the data analyzer 206 may be used inconjunction with the data tracker 208 for trends and correlationsbetween media data 302 posts such that the data collected may be used topredict future behaviors and/or plan future media events. Such events,data trends may be used in performance metrics 304, where theperformance metrics may then be used to proactively generate one or moreperformance reports for presentation in response to a user request. Forexample, the generated performance reports may be presented on adashboard interface. Since the performance reports are generated basedon real-time tracking of data, users may confidently use the informationpresented in the reports to make decisions. Further, a query may begenerated to retrieve the data and associated performance metricscorresponding to one or more domains within the enterprise system, andanother query may be generated to retrieve the data and associatedperformance metrics corresponding to one or more work flows defined bythe augmented media system 300. In response to the query, the data maybe retrieved from the database 216 and/or other external sources andpresented in an interactive user interface to the user making therequest. As indicated, performance reports may be presented on adashboard interface. In some embodiments, the data may be presented inthe form of a graph, statistics, maps, and other relevant diagrams basedon the criteria specified by the user. FIGS. 4A-4C include exemplaryinteractive interfaces that may be used in the presentation of suchdata. These exemplary interactive interfaces will be described in moredetail below and in conjunction with FIGS. 5A-5B.

In some embodiments, a social currency evaluator 204 may be part of theprocess in system 300. The social currency evaluator 204 can be used toprovide personalized content in real-time to a user. In some instances,the social currency evaluator 204 may arrive after the performancemetrics are received to provide added detail on individual's behaviorsand propensity to engage in an event. The social currency evaluator 204can further be used for profile stitching, analyzing social behaviors,and engaging key individuals to influence successful outcomes.Therefore, understanding the individual's social currency can then beused by a linking and engagement analyzer 306 for linking the behaviorswith the groups and engaging with them to impact business keyperformance indicators. In other instances, the social currencyevaluator 204 may be used prior to the performance metrics in order toperform personalized performance metrics to the user. For example, thesocial currency evaluator 204 may be used to present graphs and otherrelevant information to the user in the form of the interactive userinterfaces tailored to present the data most relevant to the individualand/or audience. Therefore, the data received, metrics collected, andsocial currency determined, may be feedback to the augmented mediasystem 202 in order to provide learned and more accurate assessments.The system 300 has a feedback loop that can create a constant stream ofself-reinforcing activity.

To illustrate an exemplary process of how an organization flow may runusing system 300, consider a marketing group within an organization. Themarketing group may use an augmented media system 202 to determine howto best market a new product for release. Concurrently, digital media iscontinually monitored for relevant events and possible crisis. Thecrises identified can then be addressed through close assessment. Theassessment can include understanding the crisis by region, timing,sentiments, etc. so that proper personalized stitching and engagementmay occur with key influencers in an effort to minimize the impactbusiness KPIs. Note that the analysis and assessments performedthroughout the process occurs using any combination of statisticalmodels, natural language processing, and artificial intelligence. Thedata analytics, as indicated above, can include the use of diagnosticanalytics, predictive, prescriptive and cognitive analytics.

As indicated, social currency evaluator 204 can be used to provideperformance metrics on an individual's or organizations behaviors aswell as for understanding how the behaviors can be used forunderstanding the impact and influence can have on a business and thebusiness key performance metrics. Individuals or organizations whosebehaviors or opinions expressed can have a significant media reach areoftentimes referred to as influencers. Influencers can contributethrough articles, analyses, tweets, videos, interviews, and the like inthe media. Influencers can have a following or people and/ororganizations that track their opinions and messages. These opinions ormessages have a reach or a number of people or organizations that readand/or engage with them. Engagement can include comments, shares,re-tweets, likes, mentions, views, etc. which can have a lasting impactor a group, organization, corporation, or other entity. Going forward,group, organization, individual, cooperation, organization, etc. will bereferred to as simply “organization.” Knowing who the influencers arewith the maximum reach and having a strategy to engage with them isbeneficial to the organization. For example, an influencer's opinion caninfluence an organization's strategy, customer's purchasing decisions,stockholder's investment strategies, etc. Therefore, knowing andunderstanding an influencer's reach may be beneficial as engagement withthe influencer can help maximize a positive message and ensure value isdelivered for the organization.

In one embodiment, the data analytics performed within the socialcurrency evaluator 204 can include an influencer module designed tounderstand an influencer reach within the augmented media intelligenceecosystem. Turning to FIG. 4, an exemplary influencer scoring model 400use to understand influencer reach within the augmented mediaintelligence ecosystem is illustrated. In particular, FIG. 4 illustratesvarious components 424-414 which may play a role in determining theinfluencer score 402. For example, the influencer score may be afunction of one or more of the components illustrated in FIG. 4. In oneembodiment, the influencer score 402, may comprise an engagementcomponent. The engagement 404 component can include a summation of allorganizations who viewed the post, tweet, video, mention, like, etc.from the influencer. Additionally, or alternatively, the engagementcomponent 404 can correspond to number of engagements (e.g., re-tweets,likes, shares, etc.) that have occurred in response to a post by theinfluencer. In another embodiment, the influencer score 402 can comprisean impressions component 406. The impressions component 406 can includethe number of impressions or views received by the post by theinfluencer. Still in another embodiment, the influencer score caninclude a reach component 408 and a publications component 410. Thereach can include a count of the number of people or organizations thatread and/or engage with the influencer. Note that the reach may be bystate, country, region, or even world wider. The publication component410 deals with the number of publications or media events published bythe influencer. Still yet in another embodiment, the influencer scorecan also include a momentum component 412 and a followers component 414.The momentum component 410, can include a measure of how fast a storybuilds based on a post, story, or other publication by the influencer.In addition, momentum can be defined by an engagement over apredetermined period of time. The followers component 414 includes asummation of the number of organizations which read the posts by theinfluencer.

Note that more or less components may be used to determine an influencerscore 402. Additionally, each of the components 404-414 may be used tocompute the influencer score 402, based on a predetermined percentage,based on the event, time period, etc. In one embodiment, each of thecomponents 404-414 may be used to determine the influencer score 402 atleast based in part on a weighted average. In one example, theinfluencer score 402 may be a function of a percentage of each of thecomponents 404-414. In this example, each component may provide afraction of the total influencer score 402, for instance, engagements404 (20%), impressions 406 (10%), reach 408 (20%), publications 410(20%), momentum 412 (10%), and followers 414 (10%). Once the influencerscore 402 is determined, this score along with other details may bereported using the augmented media intelligence ecosystem of FIGS. 1-3.Note that in addition to the influencer score, other data obtained bythe augmented media intelligence ecosystem 200 in conjunction withartificial intelligence to derive the impact of media events on thebusiness' key performance indicators using correlation algorithms and/orother analytical measures.

During the tracking and monitoring of the content, interactive userinterfaces may be used for the presentation of the information. FIGS.5A-5B provide data visualizations for understanding and illustratinginfluencer details using the augmented media intelligence ecosystem. Inparticular, FIGS. 5A-5B illustrate exemplary interactive user interfacesthat may be presented to a user of the augmented media system 202.Turning to FIG. 5A, a first exemplary interactive user interface 500 ispresented. The first exemplary interactive user interface 500illustrates a page on a dashboard of the augmented media system 202designed for a team or organization trying to understand and plan how tobest respond to a media reaction to a post or other commentary by aninfluencer. As illustrated in interactive user interface 500, a generaldisplay is presented where in all influencers followed 504 and impactare illustrated. Interactive user interface 500 includes a dashboardlike display wherein various options for selection. For example, options502 can include specific details on favorable/unfavorable posts, eventtype, region, country, etc. Additionally, the interactive user interface500 can include maps 504 illustrating the various regions with highlighton those regions with the largest influence based on some predefinedkey, table, color coding scheme, etc. and can include and illustrationof those regions with maximum engagement. Further to the map 504regions, a table illustrating top mentions and corresponding regions canalso be included in the interactive user interface 500. Other options,updates, and visualizations possible on the interactive user display 500can also include graphs, charts, stats, etc. For example, a chart can beincluded which provides a graph disclosing influencer momentum 508. Thischart can explain the impact on momentum as mentions increase and thetime passes. As indicated, momentum can be used to measure of how fast astory builds based on a post, story, or other publication by theinfluencer. Thus, influencer momentum 508 can also be plotted and put ondisplay on the interactive user interface 500.

Note that further to the interactive user interfaces 500, 550 presented,other data may also be measured and presented as an indication ofresilience. For example, as indicated media sentiment can be measured,this can include likes, impressions, mentions shares, comments, and thelike. As another example, customer contact volumes may be summarized andpresented as well as net new active accounts created. Still as anotherexample, account closures or lack of use may be considered. Also notethat although the interactive user interfaces presented above and inconjunction with FIGS. 5A-5B are presented and described for a company,such customized information is available to other organizations. Forexample, a marketing group may benefit obtaining user mentions,leadership and advertainment companies can benefit from media resiliencyinformation and teams within the organization itself can also benefitand respond using such information.

To illustrate how the interactive user interfaces and understandinginfluencer reach is determined within the augmented media system 202,FIG. 6 is introduced which illustrates example process 600 that may beimplemented on a system 700 of FIG. 7. In particular, FIG. 6 illustratesa flow diagram illustrating how an augmented media system providesinfluencer information using digital media. According to someembodiments, process 600 may include one or more of operations 602-610,which may be implemented, at least in part, in the form of executablecode stored on a non-transitory, tangible, machine readable media that,when run on one or more hardware processors, may cause a system toperform one or more of the operations 602-610.

Process 600 may begin with operation 602, where data is retrieved. Aspreviously indicated, large data is constantly collected by devices,through networks, external peripherals and other means. The datareceived, scraped, and gathered is received and/or retrieved, thencleansed, transformed and loaded in a data model designed and built forthis system in some instances stored for later use. This data retrievedin real-time and/or retrieved from a database is collected oftentimesneeds to be organized and analyzed. As previously indicated, the datamay be stored and organized based on various predetermined categorieswhich are useful in not only capturing and organizing the digital mediadata retrieved, but in providing the information needed for obtaining aninfluencer measure or score. For example, in one embodiment, the digitaldata retrieved may be stored in various databases, servers, nodes, andthe like that are distinguished as product, media, campaign, leadership,etc. In another embodiment, the data retrieved may be categorized byinfluencer, source, mentions, etc.

At operation 604, from the data retrieved and categorized, influencerfactors may be extracted. In particular, at operation 604, from the dataretrieved and categorized, data including impressions, reach,publications, momentum, followers, engagements, etc. may be pulled,summed, and used in computing an influencer score at operation 606. Insome embodiments, the influencer score may be computed using a weightedaverage on the influencer factors or components.

As the influencer score is known, process 600 continues to operation608, where the influencer score in conjunction with the data retrievedmay be used for performing analytics. That is to say the data retrievedand influencer score may be used for computing an influencer ID, forranking the influencers, understanding an influencer reach by region,source, post, etc.

With the analytics in place, the results obtained may be visuallypresented at operation 610, with trends and performance metricsvisualized using at least one interactive user interface similar to theone described above and in conjunction with FIGS. 5A-5B. Further,graphs, charts, plots, tables and the like may also be displayed andvisualized using the augmented media intelligence ecosystem 200 and usedto plan, communicate, and engage with influencers so that posts aresteered in a positive direction and impact to an organizations KPI isminimized if unfavorable. Further, similar stories may also becorrelated to determine a plan for engagement with an influencer,wherein the correlated similar stories are presented on the reportgenerated. Thus, at operation 610, the performance metrics presented canbe in the form of graphs, maps, statistics, and other relevant forms ofvisualization data.

FIG. 7 illustrates an example computer system 700 in block diagramformat suitable for implementing on one or more devices of the system inFIGS. 1-6 and in particular augmented media system 202. In variousimplementations, a device that includes computer system 700 may comprisea personal computing device (e.g., a smart or mobile device, a computingtablet, a personal computer, laptop, wearable device, PDA, etc.) that iscapable of communicating with a network 726. A service provider and/or acontent provider may utilize a network computing device (e.g., a networkserver) capable of communicating with the network. It should beappreciated that each of the devices utilized by users, serviceproviders, and content providers may be implemented as computer system700 in a manner as follows.

Additionally, as more and more devices become communication capable,such as new smart devices using wireless communication to report, track,message, relay information and so forth, these devices may be part ofcomputer system 700. For example, windows, walls, and other objects maydouble as touch screen devices for users to interact with. Such devicesmay be incorporated with the systems discussed herein.

Computer system 700 may include a bus 710 or other communicationmechanisms for communicating information data, signals, and informationbetween various components of computer system 700. Components include aninput/output (I/O) component 704 that processes a user action, such asselecting keys from a keypad/keyboard, selecting one or more buttons,links, actuatable elements, etc., and sending a corresponding signal tobus 710. I/O component 704 may also include an output component, such asa display 702 and a cursor control 708 (such as a keyboard, keypad,mouse, touchscreen, etc.). In some examples, I/O component 704 otherdevices, such as another user device, a merchant server, an emailserver, application service provider, web server, a payment providerserver, and/or other servers via a network. In various embodiments, suchas for many cellular telephone and other mobile device embodiments, thistransmission may be wireless, although other transmission mediums andmethods may also be suitable. A processor 718, which may be amicro-controller, digital signal processor (DSP), or other processingcomponent, that processes these various signals, such as for display oncomputer system 700 or transmission to other devices over a network 726via a communication link 724. Again, communication link 724 may be awireless communication in some embodiments. Processor 718 may alsocontrol transmission of information, such as cookies, IP addresses,images, and/or the like to other devices.

Components of computer system 700 also include a system memory component714 (e.g., RAM), a static storage component 714 (e.g., ROM), and/or adisk drive 716. Computer system 700 performs specific operations byprocessor 718 and other components by executing one or more sequences ofinstructions contained in system memory component 712 (e.g., forengagement level determination). Logic may be encoded in a computerreadable medium, which may refer to any medium that participates inproviding instructions to processor 718 for execution. Such a medium maytake many forms, including but not limited to, non-volatile media,volatile media, and/or transmission media. In various implementations,non-volatile media includes optical or magnetic disks, volatile mediaincludes dynamic memory such as system memory component 712, andtransmission media includes coaxial cables, copper wire, and fiberoptics, including wires that comprise bus 710. In one embodiment, thelogic is encoded in a non-transitory machine-readable medium. In oneexample, transmission media may take the form of acoustic or lightwaves, such as those generated during radio wave, optical, and infrareddata communications.

Some common forms of computer readable media include, for example, harddisk, magnetic tape, any other magnetic medium, CD-ROM, any otheroptical medium, RAM, PROM, EPROM, FLASH-EPROM, any other memory chip orcartridge, or any other medium from which a computer is adapted to read.

Components of computer system 700 may also include a short rangecommunications interface 720. Short range communications interface 720,in various embodiments, may include transceiver circuitry, an antenna,and/or waveguide. Short range communications interface 720 may use oneor more short-range wireless communication technologies, protocols,and/or standards (e.g., Wi-Fi, Bluetooth®, Bluetooth Low Energy (BLE),infrared, NFC, etc.).

Short range communications interface 720, in various embodiments, may beconfigured to detect other devices with short range communicationstechnology near computer system 700. Short range communicationsinterface 720 may create a communication area for detecting otherdevices with short range communication capabilities. When other deviceswith short range communications capabilities are placed in thecommunication area of short range communications interface 720, shortrange communications interface 720 may detect the other devices andexchange data with the other devices. Short range communicationsinterface 720 may receive identifier data packets from the other deviceswhen in sufficiently close proximity. The identifier data packets mayinclude one or more identifiers, which may be operating system registryentries, cookies associated with an application, identifiers associatedwith hardware of the other device, and/or various other appropriateidentifiers.

In some embodiments, short range communications interface 720 mayidentify a local area network using a short range communicationsprotocol, such as WiFi, and join the local area network. In someexamples, computer system 700 may discover and/or communicate with otherdevices that are a part of the local area network using short rangecommunications interface 720. In some embodiments, short rangecommunications interface 720 may further exchange data and informationwith the other devices that are communicatively coupled with short rangecommunications interface 720.

In various embodiments of the present disclosure, execution ofinstruction sequences to practice the present disclosure may beperformed by computer system 700. In various other embodiments of thepresent disclosure, a plurality of computer systems 700 coupled bycommunication link 724 to the network (e.g., such as a LAN, WLAN, PTSN,and/or various other wired or wireless networks, includingtelecommunications, mobile, and cellular phone networks) may performinstruction sequences to practice the present disclosure in coordinationwith one another. Modules described herein may be embodied in one ormore computer readable media or be in communication with one or moreprocessors to execute or process the techniques and algorithms describedherein.

A computer system may transmit and receive messages, data, informationand instructions, including one or more programs (i.e., applicationcode) through a communication link 724 and a communication interface.Received program code may be executed by a processor as received and/orstored in a disk drive component or some other non-volatile storagecomponent for execution.

Where applicable, various embodiments provided by the present disclosuremay be implemented using hardware, software, or combinations of hardwareand software. Also, where applicable, the various hardware componentsand/or software components set forth herein may be combined intocomposite components comprising software, hardware, and/or both withoutdeparting from the spirit of the present disclosure. Where applicable,the various hardware components and/or software components set forthherein may be separated into sub-components comprising software,hardware, or both without departing from the scope of the presentdisclosure. In addition, where applicable, it is contemplated thatsoftware components may be implemented as hardware components andvice-versa.

Software, in accordance with the present disclosure, such as programcode and/or data, may be stored on one or more computer readable media.It is also contemplated that software identified herein may beimplemented using one or more computers and/or computer systems,networked and/or otherwise. Where applicable, the ordering of varioussteps described herein may be changed, combined into composite steps,and/or separated into sub-steps to provide features described herein.

The foregoing disclosure is not intended to limit the present disclosureto the precise forms or particular fields of use disclosed. As such, itis contemplated that various alternate embodiments and/or modificationsto the present disclosure, whether explicitly described or impliedherein, are possible in light of the disclosure. For example, the aboveembodiments have focused on the user and user device, however, acustomer, a merchant, a service or payment provider may otherwisepresented with tailored information. Thus, “user” as used herein canalso include charities, individuals, and any other entity or personreceiving information. Having thus described embodiments of the presentdisclosure, persons of ordinary skill in the art will recognize thatchanges may be made in form and detail without departing from the scopeof the present disclosure. Thus, the present disclosure is limited onlyby the claims.

What is claimed is:
 1. A system comprising: a non-transitory memorystoring instructions; and a processor configured to execute theinstructions to cause the system to: in response to a determination thatnew data associated with a social media post published on a social mediaplatform by an influencer profile is available for processing, retrievereal-time digital data corresponding to the social media post and theinfluencer profile; classify the real-time digital data retrieved;extract influencer factors from the classified real-time digital data,wherein the influencer factors comprise: a momentum calculated based ona user engagement with the social media post in relation to apredetermined period of time, and a reach of the influencer relative toeach region of a plurality of regions corresponding to a country,wherein the reach is based on a number of users that engage with thesocial media post for each region, and wherein the momentum and thereach each has a predetermined weight in a weighted average of theinfluencer factors used to calculate an influencer score for eachregion; calculate the influencer score for each region based on acombination of the real-time digital data retrieved and the influencerfactors having the predetermined weights in the weighted average of theinfluencer factors; generate a performance report for the influencerprofile based on the calculated influencer score and the real-timedigital data retrieved; and present, on an interactive user interface ofthe system, the performance report generated.
 2. The system of claim 1,wherein the influencer factors further comprise at least one of a userengagement with the influencer profile, a number of publications to thesocial media platform by the influencer profile, a number of impressionsassociated with the social media post, and a number of followers of theinfluence profile.
 3. The system of claim 1, wherein executing theinstructions further causes the system to: correlate publications by theinfluencer profile; and present the correlated publications in thegenerated performance report.
 4. The system of claim 1, wherein theperformance report includes an influencer identification number, theinfluencer identification number providing a rank of the influencerprofile.
 5. The system of claim 4, wherein the performance report ispresented as a social currency of the influencer profile.
 6. The systemof claim 1, wherein the performance report includes a regional mapillustrating one or more regions that have a maximum user engagementwith the social media post.
 7. The system of claim 1, wherein themomentum is presented in the performance report.
 8. A method comprising:in response to a determination that new data associated with a socialmedia post published on a social media platform by an influencer profileis available for processing, retrieving real-time digital datacorresponding to the social media post and the influencer profile;classifying the real-time digital data retrieved; extracting influencerfactors from the classified real-time digital data, wherein theinfluencer factors comprise: a momentum based on a user engagement withthe social media post over a predetermined period of time, and a reachof the influencer relative to each region of a plurality of regionscorresponding to a country, wherein the reach is based on a number ofusers that engage with the social media post for each region, whereinthe momentum and the reach each has a predetermined weight in a weightedaverage of the influencer factors used to calculate an influencer scorefor each region; calculating the influencer score for each region basedon a combination of the real-time digital data retrieved and theinfluencer factors having the predetermined weights in the weightedaverage of the influencer factors; generating a performance reportcorresponding to influencer profile based on the calculated influencerscore and the real-time digital data retrieved; and presenting, on aninteractive user interface, the performance report generated.
 9. Themethod of claim 8, wherein the influencer factors further comprise atleast one of a user engagement with the influencer profile, a number ofpublications to the social media platform made by the influencerprofile, a number of impressions associated with the social media post,or a number of followers of the influencer profile.
 10. The method ofclaim 8, further comprising: correlating publications to the socialmedia platform made by the influencer profile, wherein the correlatedpublications are included in the performance report generated.
 11. Themethod of claim 8, wherein the performance report includes an influenceridentification number, wherein the influencer identification numberindicates a rank of the influencer profile relative to other influencerprofiles.
 12. The method of claim 11, wherein the performance report ispresented as a social currency of the influencer profile.
 13. The methodof claim 8, wherein the performance report includes a regional mapillustrating a geographical region that corresponds to a maximum userengagement with the social media post on the social media platform. 14.The method of claim 8, wherein the presenting, on an interactive userinterface, the performance report comprises plotting the momentum in theinteractive user interface.
 15. A non-transitory machine-readable mediumhaving stored thereon machine-readable instructions executable to causea machine to perform operations comprising: in response to adetermination that new data associated with a social media postpublished on a social media platform by an influencer profile isavailable for processing, retrieving real-time digital datacorresponding to the social media post and the influencer profile;classifying the real-time digital data retrieved; extracting influencerfactors from the classified real-time digital data, wherein theinfluencer factors comprise: a momentum calculated based on a userengagement with the social media post in relation to a predeterminedperiod of time, and a reach of the influencer relative to each region ofa plurality of regions of a country, wherein the reach is based on anumber of users that engage with the social media post for each region,and wherein the momentum and the reach each has a predetermined weightin a weighted average of the influencer factors used to calculate aninfluencer score for each region; calculating the influencer score foreach region based on a combination of the real-time digital dataretrieved and the influencer factors having the predetermined weights inthe weighted average of influencer factors; generating a performancereport corresponding to influencer profile based on the calculatedinfluencer score and the real-time digital data retrieved; andpresenting, on an interactive user interface, the performance reportgenerated.
 16. The non-transitory medium of claim 15, wherein theinfluencer factors further comprise at least one of a user engagementwith the influencer profile, a number of publications to the socialmedia platform made by the influencer profile, a number of impressionsassociated with the social media post, or a number of followers of theinfluencer profile.
 17. The non-transitory medium of claim 15, whereinthe operations further comprise: correlating publications to the socialmedia platform made by the influencer profile, wherein the correlatedpublications are included in the performance report generated.
 18. Thenon-transitory medium of claim 15, wherein the performance reportincludes an influencer identification number, wherein the influenceridentification number indicates a rank of the influencer profilerelative to other influencer profiles.
 19. The non-transitory medium ofclaim 18, wherein the performance report is presented as a socialcurrency of the influencer profile.
 20. The non-transitory medium ofclaim 15, wherein the operations further comprise presenting a graph ofthe momentum on the interactive user interface.